{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. 相对强弱市场\n",
    "*强者越强，弱者越弱*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 目录\n",
    "1. 什么是相对强弱？\n",
    "2. 怎么计算相对强弱？\n",
    "3. 如何用图形展示相对强弱？\n",
    "4. 如何用相对强弱来编写策略？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 什么是相对强弱？\n",
    "一段时间内某股票和本行业的股票或整个市场的比较，即对该股票市场表现的计量。当变动值低于1.0时，表示该股票的价格波动幅度小于整个市场；若变动值大于1.0，则表示该股票的波动大于整个市场。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 怎么计算相对强弱？\n",
    "RS = Stock/Index\n",
    "\n",
    "MOM_RS = Momentum(RS)\n",
    "\n",
    "MOM_MOM = Momentum(MOM_RS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 如何用图形展示相对强弱？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date\n",
      "2017-05-08    0.005594\n",
      "2017-05-09    0.005605\n",
      "2017-05-10    0.005699\n",
      "2017-05-11    0.005738\n",
      "2017-05-12    0.006008\n",
      "Name: close, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import tushare as ts\n",
    "\n",
    "stock = ts.get_k_data('600036', start='2016-01-01', end='2017-4-31', ktype='D', autype='qfq')\n",
    "hs300 = ts.get_hist_data('hs300', start='2016-01-01', end='2017-4-31', ktype='D', )\n",
    "\n",
    "stock.index = pd.to_datetime(stock['date'], format='%Y-%m-%d')\n",
    "hs300.index = pd.to_datetime(hs300.index, format='%Y-%m-%d')\n",
    "\n",
    "RS = stock.close/hs300.close\n",
    "RS = RS.dropna()\n",
    "print RS.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            close        RS      MOM_RS     MOM_MOM\n",
      "date                                               \n",
      "2017-05-08  18.79  0.005594  104.169014  108.494768\n",
      "2017-05-09  18.79  0.005605  103.888613  107.045956\n",
      "2017-05-10  19.02  0.005699  105.381850  107.506092\n",
      "2017-05-11  19.26  0.005738  105.704400  107.161096\n",
      "2017-05-12  20.34  0.006008  111.607811  114.556197\n"
     ]
    }
   ],
   "source": [
    "#Momentum_RS\n",
    "import talib as ta\n",
    "\n",
    "MOM_RS = ta.ROCR(RS.values, 20)*100\n",
    "MOM_MOM = ta.ROCR(MOM_RS, 20)*100\n",
    "data_s = stock.close\n",
    "data1 = pd.Series(MOM_RS, index=RS.index)\n",
    "data2 = pd.Series(MOM_MOM, index=RS.index)\n",
    "data = pd.concat([data_s, RS, data1, data2], axis=1)\n",
    "data.columns = ['close', 'RS', 'MOM_RS', 'MOM_MOM']\n",
    "print data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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0r1udr0b3dGjs+6qdqbz40zaubBtRqvnh31i6ne832YZf3tS9UXmiK6U8RInX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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa734390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "plt.plot(data.MOM_RS.tail(20).values, data.MOM_MOM.tail(20).values)\n",
    "plt.axhline(100,alpha=0.3)\n",
    "plt.axvline(100,alpha=0.3)\n",
    "X=data['MOM_RS'].iloc[-1]\n",
    "Y=data['MOM_MOM'].iloc[-1]\n",
    "plt.scatter(X,Y,color='r', s=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 如何用相对强弱来编写策略？\n",
    "**买入时机：**\n",
    "\n",
    "第一象限：（MOM_RS>100, MOM_MOM>100）\n",
    "\n",
    "第四象限：（MOM_RS< 100, MOM_MOM >100）\n",
    "\n",
    "**卖出时机**\n",
    "\n",
    "第二象限：（MOM_RS > 100, MOM_MOM < 100）\n",
    "\n",
    "第三象限：（MOM_RS< 100, MOM_MOM < 100）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "close       15.165000\n",
      "RS           0.004884\n",
      "MOM_RS     100.166950\n",
      "MOM_MOM     98.445258\n",
      "Name: 2016-03-07 00:00:00, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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oOxfYNwpa9bexeAnB/Z9vZfWBbB4d14Mv/jjOWysP85+7zv5h4/ud6Tz1dTK+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\nVxY6Ojo6Oq3y/wFr4kTcAoT92gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa50c2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#第一象限买，第二象限卖\n",
    "buy_time = []\n",
    "buy_price = []\n",
    "\n",
    "sell_time = []\n",
    "sell_price = []\n",
    "\n",
    "data = data.dropna()\n",
    "it = data.iterrows()\n",
    "t0, p0 = next(it)\n",
    "pos = 0\n",
    "\n",
    "print p0\n",
    "\n",
    "for t1, p1 in it:\n",
    "    if pos==0 and p1.MOM_RS >= 100 and p1.MOM_MOM >= 100 :\n",
    "        buy_time.append(t1)\n",
    "        buy_price.append(p1.close)\n",
    "        pos=1\n",
    "    elif pos==1 and p1.MOM_MOM < 100 :\n",
    "        sell_time.append(t1)\n",
    "        sell_price.append(p1.close)\n",
    "        pos=0\n",
    "    t0, p0 = t1, p1\n",
    "\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(data['close'])\n",
    "plt.scatter(buy_time, buy_price, c='r', marker='^', linewidths=3)\n",
    "plt.scatter(sell_time, sell_price, c='g', marker='v', linewidths=3)\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(data.MOM_RS, label = 'MOM_RS')\n",
    "plt.plot(data.MOM_MOM, label = 'MOM_MOM')\n",
    "plt.hlines(100,data.index[0],data.index[-1] , linestyles='dashed', alpha=0.5)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RUKUpFEGkpeMMP9h4gNnjErnjKq0fIxLKNLQLIj9/tYzGtk4e/4TWjxEJdRq5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbefc7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "money = np.array(sell_price) - np.array(buy_price[:-1])\n",
    "money = list(money)\n",
    "money.insert(0,0)\n",
    "money = np.array(money)\n",
    "capital = money.cumsum()\n",
    "plt.plot(capital)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作业\n",
    "根据以上代码做出其他象限进场的策略"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
